Estimating party-user similarity in Voting Advice Applications using Hidden Markov Models

Abstract

Voting Advice Applications (VAAs) are Web tools that inform citizens about the political stances of parties (and/or candidates) that participate in upcoming elections. The traditional process that they follow is to call the users and the parties to state their position in a set of policy statements, usually grouped into meaningful categories (e.g., external policy, economy, society, etc). Having the aforementioned information, VAA can provide recommendation to users regarding the proximity/distance that a user has to each participating party. A social recommendation approach of VAAs (so-called SVAAs) calculates the closeness between each party's devoted users and the current user and ranks parties according the estimated 'party users' - user similarity. In our paper we stand on this approach and we assume that 'typical' voters of particular parties can be characterized by answer patterns (sequences of choices for all policy statements included in the VAA) and that the answer choice in each policy statement can be 'predicted' from previous answer choices. Thus, we resort to Hidden Markov Models (HMMs), which are proved to be effective machine learning tools for sequential and correlated data. Based on the principles of collaborative filtering we try to model 'party users' using HMMs and then exploit these models to recommend each VAA user the party whose model best fits their answer pattern. For our experiments we use three datasets based on the 2014 elections to the European Parliament.